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AI-based Image Captioning with Deep Learning

This project focuses on automatically generating captions for images using deep learning techniques. We leverage Convolutional Neural Networks (CNNs) for feature extraction, specifically utilizing the VGG16 architecture through fine-tuning, and Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, for caption generation. The project also includes a user-friendly web application built with Streamlit for easy interaction.

Key Components

• CNN for Feature Extraction (Fine-tuned VGG16): Utilizing the VGG16 architecture for extracting rich visual features from images through fine-tuning.
• Recurrent Neural Networks (RNNs): Processing extracted image features with LSTM networks to generate coherent captions.
• Deep Learning Fusion: Combining CNNs and RNNs to bridge the semantic gap between visual and textual information.
• Streamlit Web Application: Providing a user-friendly interface for uploading images and receiving generated captions instantly.

Benefits and Applications

• Accessibility: Enhancing accessibility for visually impaired individuals by providing descriptions for images.
• Content Understanding: Facilitating content understanding and retrieval in multimedia databases.
• Social Media: Automatically generating captions for images shared on social media platforms to improve engagement and accessibility.
• E-commerce: Enhancing product descriptions with automatically generated captions for images, leading to better user experience and increased sales.

How to Use

1. Clone the repository:

   git clone https://github.com/YourUsername/AI-based-Image-Captioning.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Streamlit app:

   streamlit run app.py
   

4. Upload images and view generated captions!

Future Improvements

• Experiment with different pre-trained CNN architectures for feature extraction.
• Explore advanced RNN architectures for caption generation.
• Enhance the web application with additional features and improvements.

Contributors

• Add your name if you contributed to this project!

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